This invention relates generally to multiple target tracking, and more particularly to a method and system for tracking multiple targets in a surveillance system.
Tracking multiple targets is important in many applications, such as, for example, video surveillance, traffic monitoring, human activity analysis, sports video analysis and so forth. In addition to tracking the location of a target, other properties of the target such as its velocity, scale etc. can also be tracked. Analysis of the track of a target enables prediction of the future path of the target so that appropriate action can be taken. For example, tracking human activities in a crowded area such as an airport is important so that unusual activities may be detected and any possible damage may be prevented.
It is easier to track targets whose appearances are distinctive since multiple independent single-target trackers can be used to track them. In such a situation, all targets other than a specific target can be viewed as background due to their distinct appearance. However, it is difficult to track multiple targets whose appearances are similar such as people in crowded spaces. Multiple target tracking is fundamentally different from single target tracking and requires complex data association logic to partition detected measurements to each individual data source, and establish their correspondence with the maintained trackers. This implies two important processes that decide the success of a multi-target tracking algorithm—tracker-measurement association and tracker filtering, which are, in essence, two interleaved properties. Further, such multiple target tracking has to deal with target occlusion, in addition to other problems associated with single target tracking. In other words, a target must be recognized and tracked even while it is occluded or blocked by other objects.
Common approaches to tackling this problem take a centralized representation of a joint association vector, which is then estimated either by exhaustive enumerations, such as joint probabilistic data association (JPDA) filter, or by probabilistic Monte Carlo optimization. However, in these methods, the computational complexity involved is tremendous, especially when a large amount of tracks and measurement data needs to be handled. Sampling-based approaches have also been proposed to model the joint likelihood function, thus estimating the combined state of all targets directly. Without resorting to explicitly computing the data association, the sampling-based approaches demonstrate the capabilities of tracking multiple targets when complex motions are present. However, due to the centralized nature of the joint state representation, the complexity of these approaches grows exponentially as the number of targets to be tracked increases.
In light of the above discussion, there is a need for a method providing reduced computational complexity for tracking multiple targets.